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Title: A Multi-Model Adaptive Kalman Filtering Approach to Power System Dynamic State Estimation

Abstract

Accurate information about dynamic states (such as rotor angle and speed of a synchronous machine) is important for monitoring and controlling power system rotor-angle stability. In this paper, a multi-model adaptive Kalman filtering (MMAKF) approach is proposed to accurately and robustly estimate power system dynamic states. This approach consists of three major steps: (i) multiple Kalman filtering approaches, i.e., the extended Kalman filter (EKF), unscented Kalman filter (UKF), ensemble Kalman filter (EnKF), and cubature Kalman filter (CKF), are run concurrently in parallel to estimate the dynamic states of a synchronous generator using phasor measurement unit data; (ii) probability indexes, which quantify the likelihood of each estimation model, are determined at each time step using hypothesis testing based on the measurement innovation; (iii) the a posteriori estimate of states is obtained using the best-fix approach. The two-area four-machine system is used to evaluate the effectiveness of the proposed MMAKF approach. It is shown through the Monte-Carlo method that the estimation accuracy and robustness of the proposed approach is better than those from any individual filtering algorithm.

Authors:
 [1];  [1]; ORCiD logo [2]
  1. State University of New York at Binghamton
  2. BATTELLE (PACIFIC NW LAB)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1603348
Report Number(s):
PNNL-SA-147878
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: Proceedings of theIEEE Power & Energy Society General Meeting (PESGM 2019), August 4-8, 2019, Atlanta, GA
Country of Publication:
United States
Language:
English
Subject:
multi-model adaptive Kalman Filtering, Power System Dynamic State Estimation, phasor measurement unit data

Citation Formats

Akhlaghi, Shahrokh, Zhou, Ning, and Huang, Zhenyu. A Multi-Model Adaptive Kalman Filtering Approach to Power System Dynamic State Estimation. United States: N. p., 2019. Web. doi:10.1109/PESGM40551.2019.8974102.
Akhlaghi, Shahrokh, Zhou, Ning, & Huang, Zhenyu. A Multi-Model Adaptive Kalman Filtering Approach to Power System Dynamic State Estimation. United States. doi:10.1109/PESGM40551.2019.8974102.
Akhlaghi, Shahrokh, Zhou, Ning, and Huang, Zhenyu. Mon . "A Multi-Model Adaptive Kalman Filtering Approach to Power System Dynamic State Estimation". United States. doi:10.1109/PESGM40551.2019.8974102.
@article{osti_1603348,
title = {A Multi-Model Adaptive Kalman Filtering Approach to Power System Dynamic State Estimation},
author = {Akhlaghi, Shahrokh and Zhou, Ning and Huang, Zhenyu},
abstractNote = {Accurate information about dynamic states (such as rotor angle and speed of a synchronous machine) is important for monitoring and controlling power system rotor-angle stability. In this paper, a multi-model adaptive Kalman filtering (MMAKF) approach is proposed to accurately and robustly estimate power system dynamic states. This approach consists of three major steps: (i) multiple Kalman filtering approaches, i.e., the extended Kalman filter (EKF), unscented Kalman filter (UKF), ensemble Kalman filter (EnKF), and cubature Kalman filter (CKF), are run concurrently in parallel to estimate the dynamic states of a synchronous generator using phasor measurement unit data; (ii) probability indexes, which quantify the likelihood of each estimation model, are determined at each time step using hypothesis testing based on the measurement innovation; (iii) the a posteriori estimate of states is obtained using the best-fix approach. The two-area four-machine system is used to evaluate the effectiveness of the proposed MMAKF approach. It is shown through the Monte-Carlo method that the estimation accuracy and robustness of the proposed approach is better than those from any individual filtering algorithm.},
doi = {10.1109/PESGM40551.2019.8974102},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}

Conference:
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